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104 results for “hybrid models”
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
MicroCT scans of a hybrid poplar leaf dehydrating, with annotated slices for model training
<p>Dataset of a leaf segment of a hybrid poplar (<em>P. maximowiczii x P. nigra</em> ‘Max3’) leaf scanned using microcomputed tomography (microCT) over time as it dehydrates.</p> <p> </p> <p><strong>Data acquisition methodology</strong></p> <p>Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland). Before microCT scanning, a young fully expanded leaf was detached from the plant and a short strip (0.4 x 1.5 cm) was cut between second-order veins. The base of the strip was wrapped in polyimide tape and inserted into a styrofoam block fixed on a sample holder. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and a magnification of 40x, yielding a final voxel size of 0.1625 µm (field of view: ~416x416x312 µm). The leaf was left to dehydrate in the holder and additional scans were taken 10, 20, 25, and 30 minutes after the initial scan. Scanned projections were reconstructed to a transverse view using both absorption (gridrec; Marone <em>et al.</em> 2012) and phase contrast enhancement (Paganin <em>et al.</em> 2002) reconstruction.</p> <p> </p> <p><strong>Dataset description</strong></p> <p>On the reconstructed images a region of interest was identified using a paradermal view (i.e. top to bottom of the leaf) and used to manually align the scans of each time step. Thereafter, all images were cropped to that ROI, ensuring that the same region of the leaf was present in all image stacks.</p> <p>For all stacks, files start with:<br> <em>DEHYDRATION_small_Leaf4_time_N_</em><br> where N is the time point, with values from 1 to 5 equaling 0, 10, 20, 25, and 30 minutes.</p> <p>Following this prefix is either GRID (gridrec reconstruction), PAGANIN (phase contrast enhancement reconstruction), or LABELLED (hand labelled slices or ground truth). For GRID and PAGANIN, 8-bit grayscale stacks are provided. The AOI suffix indicates the region of interest.</p> <p>Stacks have been hand labelled over three orientations (for visual examples of the orientations see <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time1.png?versionId=26fc15aa-702e-4052-b162-702cc567634c">Labeled_Sections_order_time1.png</a> and <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time2.png">Labeled_Sections_order_time2.png</a>):</p> <ol> <li>CROSS (cross sectional, or transverse, view)</li> <li>LONGI (longitudinal view: similar to cross sectional view but starting normal to it, i.e. along the depth of the stack starting from the left of the cross-sectional view)</li> <li>PARADERMAL (top to bottom view: starting at the upper epidermis)</li> </ol> <p>A general idea of the slice range within one LABELLED stack is presented after the orientation, as:<br> <em>STARTtoENDbyRANGE</em><br> The exact position of the labelled slices for each time point can be found in the <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_slices_positions.txt?versionId=93d7e22f-9f07-4f98-8c49-d93e9a2e1ce5">Labeled_slices_positions.txt </a>file. <strong>Note that one-based indexing is used (as in ImageJ), not zero-based indexing (as in e.g. Python).</strong></p> <p> </p> <p><strong>References</strong></p> <p>Marone F, Stampanoni M. 2012. Regridding reconstruction algorithm for realtime tomographic imaging. Journal of Synchrotron Radiation 19: 1029–1037.</p> <p>Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy 206: 33–40.</p>
Mainshock+aftershock M4.95+ seismicity forecasts derived from the Regional Earthquake Likelihood Models (RELM) and the multiplicative hybrid earthquake models developed by Rhoades et al. (2014)
<p>Contains six mainshock+aftershock seismicity forecasts developed by the Working Group of the Regional Earthquake Likelihood Models (RELM) experiment, sixteen multiplicative hybrid forecasts created by Rhoades et al. (2014), and the 2011-2020 M4.95+ ANSS earthquake catalog for California. Six additional forecast files are included to properly conduct the comparative tests implemented in the Collaboratory for the Study of Earthquake Predictability (CSEP) testing centre.</p> <p>Forecasts are stored in tab separated value files with the following fields (the first row of data is shown as an example):</p> <pre>LON_0 LON_1 LAT_0 LAT_1 DEPTH_0 DEPTH_1 MAG_0 MAG_1 RATE FLAG -125.4 -125.3 40.1 40.2 0.0 30.0 4.95 5.05 5.8499099999999998e-04 1 </pre> <p>Forecast are described in detail by the following publications:</p> <p>Bird, P., and Z. Liu (2007). Seismic Hazard Inferred from Tectonics: California. Seismological Research Letters, 78(1):37-48.</p> <p>Ebel, J. E., D. W. Chambers, A. L. Kafka, and J. A. Baglivo (2007). Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California. Seismological Research Letters, 78(1): 57-65.</p> <p>Helmstetter, A., Y. Y. Kagan, and D. D. Jackson (2007). High-resolution Time-independent Grid-based Forecast for M >= 5 Earthquakes in California. Seismological Research Letters, 78(1): 78-86.</p> <p>Holliday, J., Chen, C., Tiampo, K., Rundle, J., Turcotte, D., and Donnellan, A. (2007). A RELM earthquake forecast based on pattern informatics. Seismological Research Letters, 78(1):87–93.</p> <p>Kagan, Y. Y., D. D. Jackson, and Y. Rong (2007). A Testable Five-Year Forecast of Moderate and Large Earthquakes in Southern California Based on Smoothed Seismicity. Seismological Research Letters, 78(1): 94-98.</p> <p>Rhoades, D.A., Gerstenberger, M.C., Christophersen, A., Zechar, J.D., Schorlemmer, D., Werner, M.J. and Jordan, T.H., 2014. Regional earthquake likelihood models II: Information gains of multiplicative hybrids. Bulletin of the Seismological Society of America, 104(6):3072-3083.</p> <p>Shen, Z.-K., D. D. Jackson, and Y. Y. Kagan (2007). Implications of Geodetic Strain Rate for Future Earthquakes, with a Five-Year Forecast of M5 Earthquakes in Southern California. Seismological Research Letters, 78(1):116-120.</p> <p>Ward, S. (2007). Methods for evaluating earthquake potential and likelihood in and around California. Seismological Research Letters, 78(1):121–133.</p> <p>Wiemer, S. and Schorlemmer, D. (2007). ALM: An asperity-based likelihood model for California. Seismological Research Letters, 78(1):134–140.</p>
Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials
<p>Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation.</p> <p>Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.</p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation
<p>This repository contains the datasets used in the research article "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation".</p> <p>The repository contains the following files: 1) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 2) Output - contains the final deep learning models and the outputs (evaporation and transpiration stress factor) outputs from the hybrid model developed in the study.</p> <p>Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF file formats.</p> <p>The codes related to the research article and deep learning model are available in the following repository: https://github.com/akashkoppa/StressNet</p>
Analytical modeling of an hybrid power module based on diamond and SiC devices
<p>This dataset contains the raw data used for the publication (available here : <a href="https://doi.org/10.1016/j.diamond.2022.108936">10.1016/j.diamond.2022.108936</a> ).</p> <p><strong>Analytical modeling of an hybrid power module based on diamond and SiC devices</strong></p> <p>Marine Couret, Anne Castelan, Nazareno Donato, Florin Udrea, Julien Pernot, Nicolas Rouger</p> <p>Detailed descriptions for each file can be found in "Dataset_Description.docx".</p>
Data used for the article "Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data"
<p>Data used to obtain the results presented in the article "Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data".</p> <ul> <li>CNRS_PROMES_DNI_2020-09-03_2021-01-11.zip : contains DNI measurements taken at PROMES-CNRS laboratory in Odeillo.</li> <li>The other zipped files contain image data taken at PROMES-CNRS laboratory in Odeillo. Each zipped file contains all images for one day (the date is given in the file name).</li> </ul> <p>Images and GHI measures from the following days have been used for training and cross-validation:</p> <ol> <li>2020-09-11</li> <li>2020-09-12</li> <li>2020-09-16</li> <li>2020-09-19</li> <li>2020-09-20</li> <li>2020-09-21</li> <li>2020-09-22</li> <li>2020-09-23</li> <li>2020-09-24</li> <li>2020-09-29</li> <li>2020-10-01</li> </ol> <p>Images and GHI measures from the following days have been used for test:</p> <ol> <li>2020-10-04</li> <li>2020-10-05</li> <li>2020-10-08</li> <li>2020-11-05</li> <li>2020-11-15</li> </ol>
Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
<p>We provide here the underlying data of the publication "Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose". Please find the abstract below.</p> <p>Mechanism-based kinetic models are rigorous tools to analyze enzymatic reactions, but their extension to actual conditions of the biocatalytic synthesis can be difficult. Here, we demonstrate (mechanistic-empirical) hybrid modeling for systematic optimization of the sucrose phosphorylase-catalyzed glycosylation of glycerol from sucrose, to synthesize the cosmetic ingredient α-glucosyl glycerol (GG). The empirical model part was developed to capture nonspecific effects of high sucrose concentrations (up to 1.5 M) on microscopic steps of the enzymatic trans-glycosylation mechanism. Based on verified predictions of the enzyme performance under initial rate conditions (Level 1), the hybrid model was expanded by microscopic terms of the reverse reaction to account for the full-time course of GG synthesis (Level 2). Lastly (Level 3), the application of the hybrid model for comprehensive window-of-operation analysis and constrained optimization of the GG production (~250 g/L) was demonstrated. Using two candidate sucrose phosphorylases (from <em>Leuconostoc mesenteroides</em> and <em>Bifidobacterium adolescentis</em>), we reveal the hybrid model as a powerful tool of “process decision making” to guide rational selection of the best-suited enzyme catalyst. Our study exemplifies a closing of the gap between enzyme kinetic models considered for mechanistic research and applicable in technologically relevant reaction conditions; and it highlights the important benefit thus realizable for biocatalytic process development.</p>
Preserving and sharing born-digital and hybrid objects from and across the National Collection (Decision-Making Model)
<p>When considering the complex challenges faced by cultural heritage organisations in collecting, preserving and sharing born digital and hybrid objects, it becomes clear that the process of defining solutions as a community of practice is in its early probing phase: characterised as tentative, exploratory, questioning, experimental. The workshops within this Preserving and sharing born-digital and hybrid objects from and across the National Collection project, which examined the case studies from multiple angles, yielded a richly discursive examination of the main considerations.</p> <p>This Decision Model represents an attempt to create a structured representation of those main considerations and the discourse from the workshops, to codify the main decision-making processes that an organisation may go through when assessing an acquisition of such an object, categorised into high level areas. It attempts to create a traversable system that could be used by collections professionals in their work - policy makers, managers, collections management or digital preservation practitioners, conservators.</p>
Figure 1: Venn diagram of a DSm hybrid model for a 3D frame.
<p>As another simple example of hybrid DSm model, let's consider the 3D case with the frame £ = fµ1; µ2; µ3g with the model M 6= Mf in which we force all possible conjunctions to be empty, but µ1 \ µ2. This hybrid DSm model is then represented with the Venn diagram on Fig. 1 (where boundaries of intersection of µ1 and µ2 are not precisely de¯ned if µ1 and µ2 represent only fuzzy concepts like smallness and tallness by example).</p>
Figure 2: Venn Diagram for the hybrid model for this example.
<p>Let's consider L = fL0;L1;L2;L3; L4;L5;L6g a set of ordered linguistic labels.<br> For example, L1, L2, L3, L4 and L5 may represent the values: L1 , very poor,<br> L2 , poor, L3 , medium, L4 , good and L5 , very good. Let's consider also<br> the frame £ = fA;B;C;Dg with the hybrid model corresponding to the Venn<br> diagram on Figure 2.</p>
Figure 7. Neural Network model-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>In Probabilistic Neural Network the operations are organized into a multilayer feed forward<br> neural network with four layers like input layer, hidden layer, pattern layer and output layer. PNN<br> use the Euclidean distance measure the difference between one neuron to other neurons. The actual<br> target values are stored in the hidden neuron and the optimized weighted values are fed into the<br> same category hidden neuron. Then finally the output layer compared the weighted votes of each<br> target values and the target votes are used to predict the emotions.</p>
Analysis of heritage stones and model wall paintings by pulsed laser excitation of Raman, laser-induced fluorescence and laser-induced breakdown spectroscopy signals with a hybrid system
<p>Analysis of heritage stone samples, alabaster, gypsum, limestone and marble, and model wall paintings was carried out with a laboratory, hybrid system based on the pulsed laser excitation of Raman, laser-induced fluorescence and laser-induced breakdown spectroscopy signals. The system is based on a nanosecond Q-switched Nd:YAG laser operating at its second (532 nm), third (355 nm) and fourth (266 nm) harmonics and a spectrograph coupled to a time-gated intensified charge coupled device for spectral analysis allowing detection with temporal resolution. For the stone samples, Raman spectra display the characteristic vibration modes of SO<sub>4</sub><sup>2-</sup> of calcium sulfate, in alabaster and gypsum, and of free CO<sub>3</sub><sup>2- </sup>of calcium carbonate, in limestone and marble. Simultaneously acquired laser-induced fluorescence spectra reveal characteristic bands that help to distinguish between heritage stone types. The elemental composition of stone samples is obtained by laser-induced breakdown spectroscopy upon excitation at 355 nm. Spectra of all stone samples reveal their elemental composition that includes Ca, Na, Mn and Sr and the presence of molecular species, such as CN, C<sub>2</sub> and CaO. Additional emission lines, ascribed to Mg, Si, Al and K, appear with different intensities according to the nature of the stone material. Model wall paintings, based on a red pigment, prepared as fresco or mixed with two different binders, were also studied. The complementary information provided by the three spectroscopic modes allows the identification of the pigment as red vermillion and of the different preparations based on the pigment alone or in mixtures with linseed oil and egg yolk binders.</p>
Finite element mesh of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer
<p>Image-Based Simulation (IBSim) mesh:<br> A finite element mesh of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The FE mesh data uses the EnSight Gold file format and may be visualised using Paraview (<a href="https://www.paraview.org">https://www.<strong>paraview</strong>.org</a>).</p> <p>The CT data used for the mesh is available as a separate dataset:</p> <p>This data was used originally for the following publications (please cite if re-using the data):<br> Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>
Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
<p>20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.</p>
HETEAC – The Hybrid End-To-End Aerosol Classification model for EarthCARE: Look-Up Table (LUT) for aerosol mixtures
<p>The dataset contains the look-up table (LUT) of EarthCARE’s Hybrid End-To-End Aerosol Classification (HETEAC) model. The LUT contains optical and radiative parameters for four pure aerosol components (fine mode weakly absorbing, fine mode strongly absorbing, coarse mode spherical and coarse mode non-spherical) and their mixtures. In total, 314 aerosol mixtures are considered. The LUT returns the mixing state of an aerosol mixture based on the lidar ratio and the particle linear depolarization ratio at 355 nm. The mixing state is expressed in terms of relative volume contribution of the four pure aerosol components. Additionally, the LUT returns the effective radius, the asymmetry parameter, the single scattering albedo (at 355, 532, 550, 670, 865, 1064, 1650 and 2210 nm) and the Angstrom exponent (at 28 wavelength combinations) of the aerosol mixture. The lidar ratio and the particle linear depolarization ratio is also provided at 532, 550, 670, 865, 1064, 1650 and 2210 nm.</p> <p>The datafile contains two top-level groups: the HeaderData, which contains the header variables, and the ScienceData with the variables. The latter contains two groups, the AerosolComponents, which includes the aerosol-component-related optical and microphysical variables, and the LookUpTable, which contains the HETEAC LUT variables.</p> <p>The variables included in the datafile are listed below. For each variable, a full description is provided in the long_name attribute.</p> <ul> <li>HeaderData <ul> <li>angstrom_exponent_header</li> </ul> </li> <li>ScienceData <ul> <li>AerosolComponents <ul> <li>backscatter</li> <li>effective_radius</li> <li>extinction</li> <li>logarithmic_width</li> <li>mode_radius_number</li> <li>mode_radius_volume</li> <li>particle_linear_depolarization_ratio</li> <li>refractive_index_imaginary</li> <li>refractive_index_real</li> <li>scattering</li> </ul> </li> <li>LookUpTable <ul> <li>angstrom_exponent</li> <li>asymmetry_parameter</li> <li>effective_radius</li> <li>lidar_ratio</li> <li>particle_linear_depolarization_ratio</li> <li>relative_volume_contribution</li> <li>single_scattering_albedo</li> </ul> </li> <li>radiation_wavelength</li> </ul> </li> </ul> <p>Contact</p> <p>For any further clarifications or expression of interest with respect to the EarthCARE LUT, please contact Ulla Wandinger (ulla.wandinger@tropos.de) and/or Athena Augusta Floutsi (floutsi@tropos.de).</p> <ul> </ul>
Nuclear induction lineshape modeling via hybrid SDE and MD approach
<div class="page"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>The temperature dependence of the nuclear-free induction decay in the presence of a magnetic-field gradient was found to exhibit motional narrowing in gases upon heating, a behavior that is opposite to that observed in liquids. This has led to the revision of the theoretical framework to include a more detailed description of particle trajectories since decoherence mechanisms depend on histories. In the case of free diffusion and single components, the new model yields the correct temperature trends. The inclusion of boundaries in the current formalism is not straightforward. We present a hybrid SDE-MD (stochastic differential equation – molecular dynamics) approach whereby MD is used to compute an effective viscosity and the latter is fed to the SDE to predict the line shape. The theory is in agreement with the experiments. This two-scale approach, which bridges the gap between short (molecular collisions) and long (nuclear induction) timescales, paves the way for the modeling of complex environments with boundaries, mixtures of chemical species, and intermolecular potentials.</p> </div> </div> </div> </div>
Nuclear induction lineshape modeling via hybrid SDE and MD approach
Open the record for dataset details and reuse information.
Enhanced reforming of mixed biomass tar model compounds using a hybrid gliding arc plasma catalytic process
<p>Datasheet for the paper; DOI: 10.1016/j.cattod.2019.05.046</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.